| Challenge: | Existing methods for detecting propaganda are noisy and lack of explainability. |
| Approach: | They propose to perform fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. |
| Outcome: | The proposed model outperforms several strong BERT-based baselines. |
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Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)
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| Challenge: | Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments. |
| Approach: | They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts. |
| Outcome: | The proposed method achieves superior performance on a large dataset for propaganda detection. |
Neural Architectures for Fine-Grained Propaganda Detection in News (D19-50)
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| Challenge: | MIC-CIS is a fine grained propaganda detection system . previous work focused on document level, labeling articles as propaganda . |
| Approach: | They propose to use different neural architectures to jointly perform propaganda detection tasks . they also investigate different ensemble schemes such as majority-voting, relax-vote, etc. |
| Outcome: | The proposed system performs sentences and fragment level propaganda detection tasks. |
Fine-Grained Propaganda Detection with Fine-Tuned BERT (D19-50)
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| Challenge: | The goal of the Fragment Level Classification task is to detect and classify textual segments that correspond to one of the 18 given propaganda techniques in a news articles dataset. |
| Approach: | They propose a model that performs word-level classification using a pre-trained language model to detect and classify propaganda fragments in a news article dataset. |
| Outcome: | The proposed model performs word-level classification using a popular pre-trained language model. |
Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels (D19-50)
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| Challenge: | The system was evaluated on a unified development set without distributing the gold labels. |
| Approach: | They propose to use fine-grained propaganda detection to build models that can explain why an article is propagandistic. |
| Outcome: | The proposed model performed on all eighteen propaganda techniques in the corpus of the shared task. |
Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles (2024.lrec-main)
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| Challenge: | Using large language models (LLMs) to detect propaganda from text is a challenge for the development of sophisticated models. |
| Approach: | They propose to use a large propaganda dataset to identify propagandistic content in text, visual, or multimodal languages to improve their models. |
| Outcome: | The proposed model performs better on a large propaganda dataset than the existing models on skewed datasets. |
NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning (D19-50)
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| Challenge: | In this paper, we describe our approach and system description for NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. |
| Approach: | They propose to use document Embeddings and LSTM to detect whether a sentence contains a propagandistic agenda. |
| Outcome: | The proposed approach ranked 21st in the NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. |
Understanding BERT performance in propaganda analysis (D19-50)
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| Challenge: | Despite the challenging nature of the shared task, our pretrained BERT model scored 0.62 F1 on the test set and ranked third among 25 teams who participated in the contest. |
| Approach: | They propose to use a dataset to fine-tune a model for propaganda analysis at sentence level to determine whether a text is 'propaganda' and to examine false-positive cases. |
| Outcome: | The proposed model scored 0.62 F1 on the test set and ranked third among 25 teams who participated in the shared task. |
Synthetic Propaganda Embeddings To Train A Linear Projection (D19-50)
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| Challenge: | Using contextualized token embeddings, we can extract features of propaganda from contextualized embeddnings without fine-tuning the large parameters of the base model. |
| Approach: | They propose a method for detecting fine-grained categories of propaganda in text by generating synthetically generated embeddings from pre-trained language models. |
| Outcome: | The proposed method is used in the first shared task in fine-grained propaganda detection at NLP4IF as Team Stalin. |
Discourse Structures Guided Fine-grained Propaganda Identification (2023.emnlp-main)
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| Challenge: | Using teacher-predicted probabilities and knowledge distillation frameworks to identify propaganda content is important. |
| Approach: | They propose to integrate local and global discourse structures for propaganda discovery and construct two teacher models for identifying PDTB-style discourse relations between nearby sentences and common discourse roles of sentences in a news article respectively. |
| Outcome: | The proposed models improve accuracy and recall of propaganda content identification at sentence-level and token-level. |
Pretrained Ensemble Learning for Fine-Grained Propaganda Detection (D19-50)
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| Challenge: | Propaganda detection is a reallife challenge that can affect how people understand news . |
| Approach: | They propose to use a manually annotated dataset to tackle the propaganda detection on sentence level classification task of NLP4IF 2019 workshop co-located with EMNLP-IJCNLP 2019 conference. |
| Outcome: | The proposed model is ranked in the first place with 68.8312 F1-score on the development dataset and in the sixth place with 61.3990 F1 score on the testing dataset. |